Bayesian Dirichlet mixture model for multivariate extremes: a re-parametrization

DOI10.1016/j.csda.2013.04.021zbMath1471.62176OpenAlexW2064474895WikidataQ116752810 ScholiaQ116752810MaRDI QIDQ1621331

Anne Sabourin, Philippe Naveau

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.04.021

zbMATH Keywords

multivariate extremesmixture modelssemiparametric Bayesian inferencereversible-jump algorithm

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Asymptotic properties of nonparametric inference (62G20) Bayesian inference (62F15) Statistics of extreme values; tail inference (62G32)

Related Items (14)

Bayesian threshold selection for extremal models using measures of surprise ⋮ Consistency of Bayesian inference for multivariate max-stable distributions ⋮ Sparse representation of multivariate extremes with applications to anomaly detection ⋮ Spectral Density Ratio Models for Multivariate Extremes ⋮ Bernstein polynomial angular densities of multivariate extreme value distributions ⋮ A modeler's guide to extreme value software ⋮ Dimension reduction in multivariate extreme value analysis ⋮ Semi-parametric modeling of excesses above high multivariate thresholds with censored data ⋮ Semiparametric bivariate modelling with flexible extremal dependence ⋮ Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes ⋮ A multivariate extreme value theory approach to anomaly clustering and visualization ⋮ Regression-type analysis for multivariate extreme values ⋮ Sparse regular variation ⋮ Likelihood estimators for multivariate extremes

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